The Comparative Toxicogenomics Database: update 2011

Abstract

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the interaction of environmental chemicals with gene products, and their effects on human health. Biocurators at CTD manually curate a triad of chemical–gene, chemical–disease and gene–disease relationships from the literature. These core data are then integrated to construct chemical–gene–disease networks and to predict many novel relationships using different types of associated data. Since 2009, we dramatically increased the content of CTD to 1.4 million chemical–gene–disease data points and added many features, statistical analyses and analytical tools, including GeneComps and ChemComps (to find comparable genes and chemicals that share toxicogenomic profiles), enriched Gene Ontology terms associated with chemicals, statistically ranked chemical–disease inferences, Venn diagram tools to discover overlapping and unique attributes of any set of chemicals, genes or disease, and enhanced gene pathway data content, among other features. Together, this wealth of expanded chemical–gene–disease data continues to help users generate testable hypotheses about the molecular mechanisms of environmental diseases. CTD is freely available at http://ctd.mdibl.org.

The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing

“…The CTD [39] (Supporting Information SI- Table 4) was used to perform analysis of the biological similarity of gold com- Enrichment of gene ontology (GO) categories of genes associated with the oxidizing agent mechanisms of action ( Figure 5A, Supporting Information SI Table 7) was carried out using the Webgestalt tool. [40] Webgestalt uses the hypergeometric test to determine whether the frequency of occurrence of genes belonging to a GO-category is significantly greater in the test set than in the reference list, using all the proteins in the database with GO-annotations as background.…”

“…[38] D) Compounds associated with the asah1 gene in the CTD. [39] to primary cell cultures to highly developed bioreactors. In order to achieve this both the descriptions of experimental metadata and the most relevant results need to be standardized with the use of ontologies and the SEURAT-1 keyword hierarchy.…”

“…[39] This strategy allows data to be integrated to construct chemical-gene-disease networks. Similar approaches can be used to summarize data from the SEURAT-1 projects and facilitate data mining and integrated data analysis.…”

“…[39] Clustering of the compounds by gene association (n = 5623) was compared to clustering by GO-associations (n = 2290) and to the co-clustering of chemicals by mode-of-action (Figure 4A and 4B). Structures of the reference compounds used in the data analysis are provided (Supporting Information SI 3, Figure S2).…”

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

“…Underlying many of these informatics approaches has been the availability of reference databases containing information about the relationship between genes, drugs and diseases, such as DrugBank [13], Pharmacogenomics Knowledge Base [14,15], and the Comparative Toxicogenomics Database [16]. The broader informatics approaches to drug repositioning have been recently reviewed [2].…”